https://github.com/duong18/BiasedWalk
BiasedWalk: Biased Sampling for Representation Learning on Graphs
Papers with Code 아카이브
Network Embedding
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Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/eqtpartners/companykg
Fast and Accurate Network Embeddings via Very Sparse Random Projection
https://github.com/esraabil/NECL
https://github.com/evantkchong/LEPAR
LNEMLC: Label Network Embeddings for Multi-Label Classification
https://github.com/fahrbach/icml-2020-faster-graph-embeddings
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
https://github.com/fdudsdelab/Subg-Con
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
https://github.com/ftheberge/GraphMiningNotebooks
https://github.com/ink-usc/request
https://github.com/leihuayi/NetworkEmbedding
https://github.com/liuxinkai94/Graph-embedding
https://github.com/lukashedegaard/dage
https://github.com/nanzhaogang/contrib/tree/master/application/mvn2vec
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
https://github.com/ndalsanto/PDE-DNN
Data driven approximation of parametrized PDEs by Reduced Basis and Neural Networks
https://github.com/ninghaohello/Polysemous-Network-Embedding
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
https://github.com/ninoxjy/graph-embedding
https://github.com/pygod-team/pygod
https://github.com/rmitbggroup/ICDE2020TemporalNetworkEmbedding
Temporal Network Representation Learning via Historical Neighborhoods Aggregation
https://github.com/rootlu/MMDNE
https://github.com/sezinata/MANE
https://github.com/sezinata/mvn2vec-code
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
https://github.com/shenweichen/GraphEmbedding
https://github.com/snap-stanford/graphwave
struc2vec: Learning Node Representations from Structural Identity
https://github.com/tutelalabs/tutela-app
https://github.com/xhhszc/dana
https://github.com/xuehansheng/dualhgcn
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
https://github.com/yuzhimanhua/HIMECat
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
https://github.com/zehong-wang/subgraph-pooling
struc2vec: Learning Node Representations from Structural Identity
https://github.com/zhengwang100/RECT
https://github.com/zxhhh97/ABot
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.